AI innovation
AI innovation is redefining the way employers approach, select and utilize the screening software for job applicants. The positive impacts of this are profound; recruiters need not trawl through congested employment market or infinite profiles of job candidates. However, these AI employment models are incorporated into an algorithmic framework, which could often result in automated bias, such as the gender bias seen in Amazon AI employment screening models. It is therefore essential for developers and business executives to create surveillance systems for ethical standards of their automated employment screening AI systems. As a top executive at Amazon, I would advocate that we choose the right AI model for the problem. I believe that there is not a single screening model that would entirely avoid bias; however, there are parameters that can identify and resolve the errors as we proceed. Also, as a company, we should actively guard against any bias in data selection, and we should have a diverse training data set that includes different groups. Ultimately, it would be advisable that we monitor our performance using real data by running our statistical methods against real data whenever possible.
Optimizing the potency of a diversified workforce is not only a social obligation but also a strategic competitive advantage. From a business perspective, to positively impact the industry, one has to hire the market. In addition, I believe that, in the end, these AI ethics will be regulated through legal sanctions. The United States attempt to control AI algorithms is a clear indication that such legislation will probably grant the government access to the AI software development, and conduct strict scanning of the real-life consequences these AI models. The excellent thing is that by adopting appropriate AI modelling concepts, we can significantly eradicate gender bias and also establish proper ethical awareness of complex challenges and remain within the law, thereby promoting the welfare of society.